PICO and OS-ELM-LRF Based Online Learning System for Object Detection

In this paper, we propose a complete online learning framework for object detection system creatively. The framework efficiently combines Pixel Intensity Comparisons Organized in Decision Trees (PICO) and Local Receptive Fields Based Extreme Learning Machine with Online Sequential Learning Mechanism (OS-ELM-LRF). OS-ELM-LRF is the modified ELM-LRF for which we add the online sequential mechanism. In this framework, PICO is used as the object detector to obtain core candidate regions with high confidence, while OS-ELM-LRF is applied as the object classifier to recognize the specific target. This is an extremely lightweight and efficient online learning framework that can be ported to some embedded devices. To illustrate the effectiveness of this framework, we realize the face recognition system and compare it to the deep-learning-based detection system. Experimental results demonstrate that the proposed object detection framework has not only high recognition accuracy, extremely real-time performance but also remarkable online learning ability, and it can be extended for most object detection tasks in industrial production.

I. Introduction

With the rapid development of network information technology, computer vision technology is widely applied to the fields of medicine, automobile and electron. Object detection, which is an indispensable research direction in the field of computer vision, aims to obtain the position and category of the target object in the video or picture by calculation.